docker build -t pytorch .docker run --gpus=all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -it --rm -v $(pwd):/e5-mistral-7b-instruct/ pytorch bashRun prepare_dataset to create a similarity dataset with one postive and negative pair from SNLI.
python prepare_dataset.pyset the model cache folder export TRANSFORMERS_CACHE=.cache/
First, run accelerate config --config_file ds_zero3_cpu.yaml
check the sample file for Single GPU here
Below given parameter is taken from the paper for finetuning. Adjust accroding to your dataset and usecase.
accelerate launch \
--config_file ds_zero3_cpu.yaml \
peft_lora_embedding_semantic_search.py \
--dataset_name similarity_dataset \
--max_length 512 \
--model_name_or_path intfloat/e5-mistral-7b-instruct \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--learning_rate 0.0001 \
--weight_decay 0.01 \
--max_train_steps 1000 \
--gradient_accumulation_steps 2048 \
--lr_scheduler_type linear \
--num_warmup_steps 100 \
--output_dir trained_model \
--use_peftloss function copied from here -> https://github.com/RElbers/info-nce-pytorch